Processes can be controlled more precisely to give more uniform and higher quality products by the application of automatic control principles. Insofar as the application of automatic control permits plant operation, as close as feasible, to plant design constraints, and reduces manpower costs and the costs of off-specification product, economic return of invested capital can be increased. Prior to the development of computer computational power, feedback control units used sensors to measure an output variable, variable ratio or the like. The measurements could then be used to adjust the input variable according to a standard control equation, i.e., a transfer function depending on the process. However, the nature of feedback control requires undesirable process deviations to have potentially occurred prior to taking corrective action.
The introduction of fast, efficient computers into process control methodology now permits the implementation of master units receiving a multitude of different types of interdependent process inputs of multiple interdependent process units for which an optimized output can be imposed. Such master control units can achieve a significant degree of feedforward (i. e., predictive) control in very complicated processes since even a complex process control equation can be rapidly solved. Feedforward control becomes important in the optimization of the process since the controller action can be initiated based upon a prediction of where the outputs (i. e., the controlled variables) are headed.
The optimization engine can also efficiently deal with process constraints which are only indirectly influenced if at all by manipulated variables so that the best output signals are tendered for the current conditions. Individual low hierarchy controllers manipulating a single input for control of a single output cannot produce a synthesis of the overall picture. Heretofore, controlling the process associated with a plant has been the purview of the experienced human operator. In a complicated process the appropriate action or actions may not be readily apparent or may not be taken as quickly as desired for optimum operation. Further, the human controller must develop "comfort levels" of operation based on experience and rules of thumb. Comfort levels can vary drastically between different human controllers and can be a significant distance from optimum capability or constraints of the equipment. Such comfort level of operation is necessary to account for the limitations of human data processing.
U.S. Pat. No. 4,349,869 to Prett et al. describes a method for controlling and optimizing the operation of a series of interdependent processes in a plant environment where manipulation of a constrained process input variable is used to achieve feedforward/feedback control of a process output variable. In the method synthesis, input variables are subjected to measured perturbations and the dynamic effects on the outputs are noted for prediction of the future response of the processes during on-line operation.
Grosdidier et al., FCC Unit Reactor-Regenerator Control, 1992 American Control Conference Proceedings, Paper No. WA4, pp. 117-121, describes a multivariable control strategy which was implemented on a fluidized bed catalytic converter (FCC) unit in a European refinery. The FCC unit is said to be a prime candidate for advanced process control due to its complex process behavior and because of the conflict between its operating constraints and its economic objectives.
Of concern in the present invention is to control a process having inputs and outputs and having controlled variables, manipulated variables, associated variables and disturbance variables. Also, it is of concern in the present invention to optimize the relationships of variables in connection with the process having inputs and outputs related to the controlled variables, manipulated variables, associated variables and disturbance variables.
It is, therefore, a feature of the present invention to provide a method for controlling the inputs and outputs of a process having controlled variables, manipulated variables, associated variables and disturbance variables.
It is, also, a feature of the present invention to optimize the relationship of controlled variables, manipulated variables, associated variables and disturbance variables in operative association with inputs and outputs of the process.
A feature of the present invention is to provide a model predictive control algorithm that is capable of handling multiple inputs and/or outputs.
Another feature of the present invention is to provide a model predictive control algorithm that constrains manipulated variables and associated variables.
Another feature of the present invention is to provide a predictive control algorithm that performs feedforward disturbance rejection analysis.
Yet another feature of the present invention is to provide a model predictive control algorithm that uses a dynamic model in the solution of the control problem.
Still another feature of the present invention is to use a dynamic model with the control algorithm that is derived from data in actual plant experiments.
Yet another feature of the present invention is to provide an example of application of the foregoing features to maximize the feed gas flow rate of an ammonia plant subject to a set of constraints specific to the particular plant.
Still another feature of the present invention is to control the hydrogen/nitrogen molar ratio at a synthesis loop in an ammonia plant to a desired target value.
It is a general feature of the present invention to maximize the operating capacity of an ammonia plant by operating the plant at one or more of its equipment or operating constraints.
Yet further, an additional feature of the present invention is to automatically apply production rate maximizing strategies in ammonia plants without manual adjustment of the feed gas to the reformer furnace.
Still another feature of the present invention is to provide the automatic control of ammonia plants by monitoring and controlling equipment and operating constraints.
Still further, another feature of the invention is to maximize production strategies employed in ammonia plants such that typically applied comfort zones can be reduced to minimum levels.
Additional features and advantages of the invention will be set forth in part in the description which follows, and in part will become apparent from the description, or may be learned by practice of the invention. The features and advantages of the invention may be realized by means of the combinations and steps pointed out in the appended claims.